Question

我在Pipelines流程中的predict期间遇到问题，每个管道步骤都有自定义类。

class MyFeatureSelector():
    def __init__(self, features=5, method='pca'):
        self.features = features
        self.method = method

    def fit(self, X, Y):
        return self

    def transform(self, X, Y=None):
        try:
            if self.features < X.shape[1]:
                if self.method == 'pca':
                    selector = PCA(n_components=self.features)
                elif self.method == 'rfe':
                    selector = RFE(estimator=LinearRegression(n_jobs=-1),
                                   n_features_to_select=self.features,
                                   step=1)
                selector.fit(X, Y)
                return selector.transform(X)
        except Exception as err:
            print('MyFeatureSelector.transform(): {}'.format(err))
        return X

    def fit_transform(self, X, Y=None):
        self.fit(X, Y)
        return self.transform(X, Y)


model = Pipeline([
    ("DATA_CLEANER", MyDataCleaner(demo='', mode='strict')),
    ("DATA_ENCODING", MyEncoder(encoder_name='code')),
    ("FEATURE_SELECTION", MyFeatureSelector(features=15, method='rfe')),
    ("HUBER_MODELLING", HuberRegressor())
])

因此，上面的代码在这里非常有用：

 model.fit(X, _Y)

但我在这里有错误

 prediction = model.predict(XT)

错误：形状（672,107）和（15）未对齐：107（暗淡1）！= 15（暗淡 0）

调试在此处显示此问题：selector.fit(X, Y)，因为在MyFeatureSelector步骤中创建了predict()的新实例，此时Y不存在。

我哪里错了？

Answer 1

下面发布的工作版本：

class MyFeatureSelector():
    def __init__(self, features=5, method='pca'):
        self.features = features
        self.method = method
        self.selector = None
        self.init_selector()


    def init_selector():
        if self.method == 'pca':
            self.selector = PCA(n_components=self.features)
        elif self.method == 'rfe':
        self.selector = RFE(estimator=LinearRegression(n_jobs=-1),
                               n_features_to_select=self.features,
                               step=1)

    def fit(self, X, Y):
       return self

    def transform(self, X, Y=None):
        try:
            if self.features < X.shape[1]:
                if Y is not None:
                    self.selector.fit(X, Y)
                return selector.transform(X)
        except Exception as err:
            print('MyFeatureSelector.transform(): {}'.format(err))
       return X

def fit_transform(self, X, Y=None):
    self.fit(X, Y)
    return self.transform(X, Y)

在sklearn中使用Pipeline和自定义类

1 个答案: